Heart Disease, Clinical Proteomics and Mass Spectrometry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Heart disease is the leading cause of mortality and morbidity in the world. As such, biomarkers are needed for the diagnosis, prognosis, therapeutic monitoring and risk stratification of acute injury (acute myocardial infarction (AMI)) and chronic disease (heart failure). The procedure for biomarker development involves the discovery, validation, and translation into clinical practice of a panel of candidate proteins to monitor risk of heart disease. Two types of biomarkers are possible; heart-specific and cardiovascular pulmonary system monitoring markers. Here we review the use of MS in the process of cardiac biomarker discovery and validation by proteomic analysis of cardiac myocytes/tissue or serum/plasma. An example of the use of MS in biomarker discovery is given in which the albumin binding protein sub-proteome was examined using MALDI-TOF MS/MS. Additionally, an example of MS in protein validation is given using affinity surface enhanced laser desorption ionization (SELDI) to monitor the disease-induced post-translational modification and the ternary status of myocyte-originating protein, cardiac troponin I in serum.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it